[R-sig-Geo] hourly time series

[Vlad]

Hi Advait,

if you want to keep all time properties, the package "timetools" may 
help you to do so.
This package defines the class "TimeIntervalDataFrame" which can be a 
good start to look at.

Best,

Vlad

-----
Vladislav Navel

SCAN
www.scan-datamining.com

Le 13/03/2012 11:03, Advait Godbole a écrit :
> I want to run the analysis on hourly data, in the sense that I would like
> to retain the hourly resolution while still being able to identify seasonal
> variation and month-to-month (or week-to-week) trends. I am not sure
> whether I am being clear in a mathematical sense, or if the above is
> possible - please excuse, I am new to time series.
>
> Thanks!
> advait
>
> On Tue, Mar 13, 2012 at 3:22 PM, Hodgess, Erin<HodgessE at uhd.edu>  wrote:
>
>> **
>>
>> Do you want to run the analysis on the hourly data or some aggregate of
>> it, please?
>>
>> thanks,
>> erin
>>
>>
>> Erin M. Hodgess, PhD
>> Associate Professor
>> Department of Computer and Mathematical Sciences
>> University of Houston - Downtown
>> mailto: hodgesse at uhd.edu
>>
>>
>>
>>
>> -----Original Message-----
>> From: r-sig-geo-bounces at r-project.org on behalf of Advait Godbole
>> Sent: Tue 3/13/2012 4:41 AM
>> To: r-sig-geo at r-project.org
>> Subject: [R-sig-Geo] hourly time series
>>
>> Dear all,
>>
>> I have one year's worth of hourly data, starting from 1st April 2010 and
>> ending on 31st March 2011. I would like to perform time series analysis on
>> it. Not having used "ts" before, I am having trouble setting it up to
>> correctly represent the data. I have R reading in the time series via:
>> *wind_ts<- ts(wind.MH,start=1,frequency=1)*
>>
>>
>> where "wind.MH" is a 8760x1 matrix object. I then tried to decompose the
>> time series with the following error:
>> *wind_ts_components<- decompose(wind_ts)*
>> *Error in decompose(wind_ts) : time series has no or less than 2 periods*
>>
>>
>> The dataset is the hourly wind generation for Maharashtra, India and has
>> some seasonality associated with the Indian monsoon. Ultimately, this is
>> what I would like to identify. I imagine that correctly setting the "start"
>> and "frequency" parameters is necessary to be able to parse the dataset
>>   into months and seasons.
>>
>> I would greatly appreciate help on how to handle this and any leads on time
>> series analysis for hourly data in general.
>>
>> Regards,
>> --
>> advait godbole
>> analyst, prayas energy group
>> pune, india
>>
>>          [[alternative HTML version deleted]]
>>
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>